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CAMAL: Optimizing LSM-trees via Active Learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/spe.2799openalex-snapshot
Robust and efficient memory management in Apache AsterixDB
inferredcites →https://doi.org/10.1007/s002360050048openalex-snapshot
The log-structured merge-tree (LSM-tree)
inferredcites →https://doi.org/10.1007/s00778-021-00670-9openalex-snapshot
$$\hbox {CDBTune}^{+}$$: An efficient deep reinforcement learning-based automatic cloud database tuning system
inferredcites →https://doi.org/10.1007/s00778-023-00826-9openalex-snapshot
Towards flexibility and robustness of LSM trees
inferredcites →https://doi.org/10.1007/s10115-021-01605-0openalex-snapshot
Model complexity of deep learning: a survey
inferredcites →https://doi.org/10.1038/s41592-020-0772-5openalex-snapshot
Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python
inferredcites →https://doi.org/10.1088/issn.1742-6596openalex-snapshot
Journal of Physics: Conference Series
inferredcites →https://doi.org/10.1109/icde.2017.121openalex-snapshot
On Log-Structured Merge for Solid-State Drives
inferredcites →https://doi.org/10.1109/icde55515.2023.00273openalex-snapshot
The LSM Design Space and its Read Optimizations
inferredcites →https://doi.org/10.1111/j.1467-985x.2010.00646_6.xopenalex-snapshot
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1145/1807128.1807152openalex-snapshot
Benchmarking cloud serving systems with YCSB
inferredcites →https://doi.org/10.1145/2592798.2592804openalex-snapshot
An efficient design and implementation of LSM-tree based key-value store on open-channel SSD
inferredcites →https://doi.org/10.1145/2741948.2741973openalex-snapshot
Scaling concurrent log-structured data stores
inferredcites →https://doi.org/10.1145/2882903.2904441openalex-snapshot
Realtime Data Processing at Facebook
inferredcites →https://doi.org/10.1145/3035918.3064029openalex-snapshot
Automatic Database Management System Tuning Through Large-scale Machine Learning
inferredcites →https://doi.org/10.1145/3183713.3183761openalex-snapshot
Lightweight Cardinality Estimation in LSM-based Systems
inferredcites →https://doi.org/10.1145/3183713.3196909openalex-snapshot
The Case for Learned Index Structures
inferredcites →https://doi.org/10.1145/3299869openalex-snapshot
Proceedings of the 2019 International Conference on Management of Data
inferredcites →https://doi.org/10.1145/3299869.3300085openalex-snapshot
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning
inferredcites →https://doi.org/10.1145/3318464.3389731openalex-snapshot
Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores
inferredcites →https://doi.org/10.1145/3318464.3389757openalex-snapshot
Lethe: A Tunable Delete-Aware LSM Engine
inferredcites →https://doi.org/10.1145/3318464.3389768openalex-snapshot
Active Learning for ML Enhanced Database Systems
inferredcites →https://doi.org/10.1145/3448016openalex-snapshot
Proceedings of the 2021 International Conference on Management of Data
inferredcites →https://doi.org/10.1145/3448016.3457273openalex-snapshot
Chucky: A Succinct Cuckoo Filter for LSM-Tree
inferredcites →https://doi.org/10.1145/3448016.3457276openalex-snapshot
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems
inferredcites →https://doi.org/10.1145/3465998.3466002openalex-snapshot
Reducing Bloom Filter CPU Overhead in LSM-Trees on Modern Storage Devices
inferredcites →https://doi.org/10.1145/3514221.3517845openalex-snapshot
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems
inferredcites →https://doi.org/10.1145/3514221.3522563openalex-snapshot
Dissecting, Designing, and Optimizing LSM-based Data Stores
inferredcites →https://doi.org/10.1145/3514221.3526167openalex-snapshot
Proteus: A Self-Designing Range Filter
inferredcites →https://doi.org/10.1145/3588917openalex-snapshot
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data
inferredcites →https://doi.org/10.1145/3617333openalex-snapshot
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads
inferredcites →https://doi.org/10.1145/3654978openalex-snapshot
Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in a Colossal Configuration Space
inferredcites →https://doi.org/10.14778/1687627.1687767openalex-snapshot
Tuning database configuration parameters with iTuned
inferredcites →https://doi.org/10.14778/2757807.2757810openalex-snapshot
Compaction management in distributed key-value datastores
inferredcites →https://doi.org/10.14778/3329772.3329780openalex-snapshot
Selectivity estimation for range predicates using lightweight models
inferredcites →https://doi.org/10.14778/3342263.3342646openalex-snapshot
Plan-structured deep neural network models for query performance prediction
inferredcites →https://doi.org/10.14778/3450980.3450992openalex-snapshot
An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems
inferredcites →https://doi.org/10.14778/3476311.3476411openalex-snapshot
Make your database system dream of electric sheep
inferredcites →https://doi.org/10.1609/aaai.v33i01.33014039openalex-snapshot
Precision-Recall versus Accuracy and the Role of Large Data Sets
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-981-92-0363-5_38openalex-snapshot
Workload-Aware DHB+Tree: A Dynamic B+-Tree for Persistent Memory
inferred← citeshttps://doi.org/10.1016/j.future.2026.108425openalex-snapshot
C2LSM: A configuration paradigm for efficient compaction in LSM-tree-based key-value stores
inferred← citeshttps://doi.org/10.1109/icde65706.2026.00187openalex-snapshot
Nezha: A Key-Value Separated Distributed Store with Optimized Raft Integration
inferred← citeshttps://doi.org/10.1109/icde65706.2026.00278openalex-snapshot
Automatic Parameter Tuning for Compaction in Lsm-Tree Based Databases
inferred← citeshttps://doi.org/10.1145/3709662openalex-snapshot
Aster: Enhancing LSM-structures for Scalable Graph Database
inferred← citeshttps://doi.org/10.1145/3749169openalex-snapshot
Hourglass: An Adaptive Range Filter with Lightweight Hybrid Encoding
inferred← citeshttps://doi.org/10.1145/3786661openalex-snapshot
Improving Range Scan Performance in LSM-trees with Group Caching
inferred← citeshttps://doi.org/10.1145/3793230.3839381openalex-snapshot
TellyTune: Exploring the Limits of LLM-Assisted Tuning for LSM-Based Key-Value Stores
inferred← citeshttps://doi.org/10.1145/3830340.3830352openalex-snapshot
Towards AI and LSM-tree KV Stores: Progresses, Challenges and Prospects
inferred← citeshttps://doi.org/10.14778/3717755.3717767openalex-snapshot
Are Joins over LSM-Trees Ready? Take RocksDB as an Example
inferred← citeshttps://doi.org/10.14778/3773731.3773735openalex-snapshot
AXE: A Task Decomposition Approach to Learned LSM Tuning
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